Farah Bouakrif

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Andrej Dujella

University of Zagreb

Hemin Koyi

Uppsala University

Jana Javornik

University of East London

Graham Martin

University of Leicester

Gwen Robbins Schug

University of North Carolina at Greensboro

Gabriel Gutierrez-Alonso

University of Salamanca

John Sutton

Macquarie University

Eros Carvalho

Universidade Federal do Rio Grande do Sul

Kevin Arbuckle

Swansea University

Jesper Hoffmeyer

University of Copenhagen

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Papers by Farah Bouakrif

Iterative learning control schemes for robot manipulators

Mediterranean Journal of Measurement and Control

ABSTRACT

Reference model iterative learning control for linear systems

This paper deals with iterative learning control (ILC) design for nonlinear systems with repeatab... more This paper deals with iterative learning control (ILC) design for nonlinear systems with repeatable and non-repeatable uncertainties and performing repetitive tasks to follow a reference model (also called desired system). This desired system does not necessarily have the same structure, nor the same parameters as the real systems (there is no dependence between the reference model system and the real system). For this purpose, two ILC schemes are considered and analysed. The first controller assures the asymptotic stability with a simple condition to verify, whereas the second assures this stability without condition to verify. The λ-norm is adopted as the topological measure in our proof of the asymptotic stability of the closed loop system over the whole finite time interval when the iteration number tends to infinity. Finally, two simulation results on nonlinear system are provided to illustrate the effectiveness of the proposed controllers.

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Trajectory tracking control using velocity observer and disturbances observer for uncertain robot manipulators without tachometers

Meccanica, Jun 13, 2016

This paper deals with trajectory tracking control for rigid robot manipulators with model uncerta... more This paper deals with trajectory tracking control for rigid robot manipulators with model uncertainty and subject to external disturbances. The approach suggested herein does not require velocity measurement, because these robots are not equipped by tachometers for velocity measurement. For this purpose, two observers are proposed. The first is a velocity observer to estimate the missing velocity, and the second one is a disturbance observer to estimate the disturbance. Thereafter, these observers are integrated with the controller. Furthermore, semi-global asymptotic stability conditions of the composite controller consisting of a nonlinear controller, the velocity observer and the disturbance observer are established, and an estimate region of attraction is also given. This proof is based on Lyapunov theory. Finally, simulation results on two-links manipulator are provided to illustrate the effectiveness of the velocity observer based control using disturbance estimation (namely VOBCDE), when the Coulomb and viscous friction is considered as an external disturbance.

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High order iterative learning control to solve the trajectory tracking problem for robot manipulators using Lyapunov theory

Transactions of the Institute of Measurement and Control, Jan 25, 2018

This paper deals with Iterative Learning Control (ILC) design to solve the trajectory tracking pr... more This paper deals with Iterative Learning Control (ILC) design to solve the trajectory tracking problem for rigid robot manipulators subject to external disturbances, and performing repetitive tasks. A high order ILC scheme is synthetized; this controller contains the information (errors) of several iterations and not only of one iteration. It has been shown that the closed loop system (robot plus controller) is asymptotically stable, over the whole finite time interval, when the iteration number tends to infinity. This proof is based upon the use of a Lyapunov-like positive definite sequence, which is shown to be monotonically decreasing under the proposed controller scheme. Finally, simulation results on two-link manipulator are provided to illustrate the effectiveness of the proposed controller.

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Comparison between three types of iterative learning control for nonlinear systems: On-line, off-line and on-line taking into account the previous error

This paper deals with iterative learning control “ILC” to solve the trajectory tracking problem f... more This paper deals with iterative learning control “ILC” to solve the trajectory tracking problem for nonlinear continuous-time systems having a non linear output. Thus, a comparison between three types of this control scheme has been made: on-line ILC, offline ILC and on-line ILC taking into account the previous error. The λ -norm is adopted as the topological measure in our proof of the asymptotic stability of these control schemes, over the whole finite time-interval, when the iteration number tends to infinity. Simulation results on a nonlinear system showed us that on-line ILC taking into account the previous error is better in terms of speed of convergence that off-line and on-line ILC.

Iterative learning neural networks control with disturbances estimation for nonlinear systems

This paper deals with P-type iterative learning neural networks control (ILNNC) to solve the traj... more This paper deals with P-type iterative learning neural networks control (ILNNC) to solve the trajectory tracking problem for multi input multi output (MIMO) nonlinear systems with unknown varying iteration disturbance. The proposed controller takes the advantages of using just the proportional action and can be applied to nonlinear systems without using the global Lipchitz condition. Thus, the unknown norm bounded function is approximated by a radial basis function neural networks (RBFNN).The unknown disturbances at each iteration are adjusted with a disturbance estimation law. The asymptotic stability of the closed-loop system is guaranteed analytically using the Lyapunov theory over the whole finite time. Finally, a numerical example on nonlinear system is given to show the effectiveness of the proposed method.

Iterative Learning Control for Affine and Non-affine Nonlinear Systems

Studies in computational intelligence, 2016

This chapter deals with Iterative Learning Control ILC schemes to solve the trajectory tracking p... more This chapter deals with Iterative Learning Control ILC schemes to solve the trajectory tracking problem of affine and non-affine nonlinear systems performing repetitive tasks. Two ILC laws are presented; the first law is a simple on-line 2D-type learning control for affine nonlinear systems. In addition, an initial condition algorithm is generated to provide the initial state value at each iteration automatically. To prove the asymptotic stability of the closed loop system over the whole finite time interval when the iteration number tends to infinity, \(\lambda \)-norm is used, as the topological measure. The second law is the on-line P-type ILC applied to non affine nonlinear systems. The asymptotic stability of the closed loop system is guaranteed upon the use of a Lyapunov-like positive definite sequence, which is shown to be monotonically decreasing under the proposed control scheme. Finally, simulation results on nonlinear system are provided to illustrate the effectiveness of the two controllers.

Iterative learning radial basis function neural networks control for unknown multi input multi output nonlinear systems with unknown control direction

Transactions of the Institute of Measurement and Control, Feb 19, 2019

In this paper, an iterative learning radial basis function neural-networks (RBF NN) control algor... more In this paper, an iterative learning radial basis function neural-networks (RBF NN) control algorithm is developed for a class of unknown multi input multi output (MIMO) nonlinear systems with unknown control directions. The proposed control scheme is very simple in the sense that we use just a P-type iterative learning control (ILC) updating law in which an RBF neural network term is added to approximate the unknown nonlinear function, and an adaptive law for the weights of RBF neural network is proposed. We chose the RBF NN because it has universal approximation capabilities and can approximate any continuous function. In addition, among the advantages of our controller scheme is the fact that it is applicable to deal with a class of nonlinear systems without the need to satisfy the global Lipschitz continuity condition and we assume, only, that the unstructured uncertainty is norm-bounded by an unknown function. Another advantage of the proposed controller and unlike other works on ILC, we do not need any prior knowledge of the control directions for MIMO nonlinear system. Thus, the Nussbaum-type function is used to solve the problem of unknown control directions. In order to prove the asymptotic stability of the closed-loop system, a Lyapunov-like positive definite sequence is used, which is shown to be monotonically decreasing under the control design scheme. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed control scheme.

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Iterative learning neural networks control with disturbances estimation for nonlinear systems

2019 4th World Conference on Complex Systems (WCCS)

Trajectory tracking controller for nonlinear systems with disturbances using iterative learning algorithm without resetting condition

2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)

This paper presents an iterative learning scheme (PD-type) to solve the trajectory tracking probl... more This paper presents an iterative learning scheme (PD-type) to solve the trajectory tracking problem for repetitive uncertain nonlinear systems. This scheme consists of two parts, the first is an iterative learning controller and the second is an algorithm which gives us the initial state at each trial. λ-norm method is used to prove the asymptotic stability of the closed loop system. Finally, we apply this controller scheme on perturbed nonlinear system to show its effectiveness.

Iterative Learning Fuzzy control with Optimal Gains for a Class of Nonlinear systems

2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), 2019

This paper proposes a novel P-type iterative learning fuzzy control with optimal gains for a clas... more This paper proposes a novel P-type iterative learning fuzzy control with optimal gains for a class of Multi Input Multi Output (MIMO) nonlinear systems. The control design is very simple, in the sense that we use just a proportional learning action. Another advantage of this proposed controller is that, the global Lipschitz condition is not required for nonlinear systems. Thus, to approximate the unknown nonlinear function, we use a fuzzy logic term. In addition, the swarm optimization algorithm is used to design the optimum iterative learning fuzzy control (ILFC), in the sense that the tracking errors converge at the fastest rate. To prove the asymptotic stability of the closed loop system over the whole finite time, Lyapunov theory is used. Finally and to illustrate the effectiveness of the proposed control scheme, simulation results are presented.

Iterative Learning Fuzzy Control for Nonlinear Systems with Adaptive Gain and without Resetting Condition

Cybernetics and Systems, 2021

Abstract In this paper, we present two ILC schemes. The first one is a PD-type iterative learning... more Abstract In this paper, we present two ILC schemes. The first one is a PD-type iterative learning control with an initial state algorithm to solve the trajectory tracking problem for nonlinear systems with uncertainties and without satisfying the classical resetting condition. -norm method is used to prove the asymptotic stability of the closed loop system and the simulation results on perturbed nonlinear system have been given. The second approach is a simple P-type iterative learning fuzzy control scheme to solve the trajectory tracking problem for MIMO nonlinear systems. The control design is applicable to deal with a class of nonlinear systems without satisfying the global Lipschitz continuity condition, for which a fuzzy logic term is added to cope with unknown parameters. In addition, the swarm optimization algorithm is used to design the optimum iterative learning fuzzy control (ILFC). Using Lyapunov theory, the asymptotic stability of the closed loop system is guaranteed over the whole finite time. Finally, an illustrative example on two-link manipulator is provided to illustrate the effectiveness of the proposed controller.

Iterative Learning Control for Affine and Non-affine Nonlinear Systems

Advances and Applications in Nonlinear Control Systems, 2016

Disturbance obsever based iterative learning control for robot manipulators

In this paper, usin a Lyapunov-like function, we derive a disturbance observer based iterative le... more In this paper, usin a Lyapunov-like function, we derive a disturbance observer based iterative learning control scheme for the trajectory tracking problem of rigid robot manipulator. In this control scheme, the whole control law consists of two parts, the feedback control law, plus an iteratively updated term represents the estimated disturbance. The feedback control law using in this paper is a computed torque control, without compensating for the gravity forces. Using Lyapunov method, the asymptotic stability of the whole system is guaranteed, and the external disturbances with the gravity forces are compensated. Simulation results on the PUMA 560 robot manipulator, show the asymptotic convergence of tracking error, when the Coulomb and Viscous friction is considered as an external disturbance.

Commande par apprentissage itératif à base d’un modèle de référence des systèmes linéaires avec des perturbations répétées et non répétées

Commande par apprentissage iteratif a base d’un modele de reference des systemes lineaires avec d... more Commande par apprentissage iteratif a base d’un modele de reference des systemes lineaires avec des perturbations repetees et non repetees Farah Bouakrif Laboratoire LAMEL Universite de Jijel, Algerie Resume : Cet article porte sur la commande par apprentissage iteratif des systemes lineaires repetitifs avec des perturbations repetees et non repetees afin de suivre un modele de reference. Ce dernier n’a aucune dependance avec le modele du systeme a commander, et les etats initiaux de deux systemes ne doivent pas forcement etre egaux. En utilisant λ norme comme une methode topologique, la stabilite asymptotique du systeme en boucle fermee est demontree, dans un intervalle de temps fini lorsque le nombre des iterations tend vers l’infini. Les resultats de simulations sur un systeme lineaire prouvent clairement l’efficacite de la commande par apprentissage iteratif presentee. Mot cles : Commande par apprentissage iteratif, modele de reference, λ norme, perturbations repetees, perturbat...

Iterative learning control for robot manipulators

by Farah Bouakrif and Djamel BOUKHETALA

Archives of Control Sciences, 2007

Iterative Learning and Fractional Order Control for Complex Systems

Complexity, 2019

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Adaptive P‐type iterative learning radial basis function control for robot manipulators with unknown varying disturbances and unknown input dead zone

International Journal of Robust and Nonlinear Control, 2020

SummaryThis article proposes an adaptive iterative learning radial basis function (RBF) scheme to... more SummaryThis article proposes an adaptive iterative learning radial basis function (RBF) scheme to solve the trajectory‐tracking problem for perturbed robot manipulators with unknown iteration varying disturbances and unknown dead‐zone input. It is well known that the presence of the dead zone in actuators and mechatronics devices gives rise to extra difficulty due to the presence of singularity in the input channels. Hence, it is interesting to take this problem into account when synthesizing a controller. This synthesis is made here. In addition, the control design is very simple in the sense that we use, only, the proportional gain. Therefore, the considerable amount noise caused by the sensors for velocity measurements of robot manipulators is avoided. Another advantage of this work is that the unknown disturbances are assumed to be time varying and also varying from iteration to iteration. Thus, the RBF neural network is used to approximate these unknown nonlinear functions. Usi...

Iterative learning radial basis function neural networks control for unknown multi input multi output nonlinear systems with unknown control direction

Transactions of the Institute of Measurement and Control, 2019

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High order iterative learning control to solve the trajectory tracking problem for robot manipulators using Lyapunov theory

Transactions of the Institute of Measurement and Control, 2018

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Iterative learning control schemes for robot manipulators

Mediterranean Journal of Measurement and Control

ABSTRACT

Reference model iterative learning control for linear systems

Download

Trajectory tracking control using velocity observer and disturbances observer for uncertain robot manipulators without tachometers

Meccanica, Jun 13, 2016

Download

High order iterative learning control to solve the trajectory tracking problem for robot manipulators using Lyapunov theory

Transactions of the Institute of Measurement and Control, Jan 25, 2018

Download

Comparison between three types of iterative learning control for nonlinear systems: On-line, off-line and on-line taking into account the previous error

Iterative learning neural networks control with disturbances estimation for nonlinear systems

Iterative Learning Control for Affine and Non-affine Nonlinear Systems

Studies in computational intelligence, 2016

Iterative learning radial basis function neural networks control for unknown multi input multi output nonlinear systems with unknown control direction

Transactions of the Institute of Measurement and Control, Feb 19, 2019

Download

Iterative learning neural networks control with disturbances estimation for nonlinear systems

2019 4th World Conference on Complex Systems (WCCS)

Trajectory tracking controller for nonlinear systems with disturbances using iterative learning algorithm without resetting condition

2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)

Iterative Learning Fuzzy control with Optimal Gains for a Class of Nonlinear systems

2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), 2019

Iterative Learning Fuzzy Control for Nonlinear Systems with Adaptive Gain and without Resetting Condition

Cybernetics and Systems, 2021

Iterative Learning Control for Affine and Non-affine Nonlinear Systems

Advances and Applications in Nonlinear Control Systems, 2016

Disturbance obsever based iterative learning control for robot manipulators

Commande par apprentissage itératif à base d’un modèle de référence des systèmes linéaires avec des perturbations répétées et non répétées

Iterative learning control for robot manipulators

by Farah Bouakrif and Djamel BOUKHETALA

Archives of Control Sciences, 2007

Iterative Learning and Fractional Order Control for Complex Systems

Complexity, 2019

Download

Adaptive P‐type iterative learning radial basis function control for robot manipulators with unknown varying disturbances and unknown input dead zone

International Journal of Robust and Nonlinear Control, 2020

Iterative learning radial basis function neural networks control for unknown multi input multi output nonlinear systems with unknown control direction

Transactions of the Institute of Measurement and Control, 2019

Download

High order iterative learning control to solve the trajectory tracking problem for robot manipulators using Lyapunov theory

Transactions of the Institute of Measurement and Control, 2018

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